Social Pressure in Opinion Games
Authors: Diodato Ferraioli, Carmine Ventre
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove that for clique social networks, the dynamics always converges to consensus (no matter the level of noise) if the social pressure is high enough. Moreover, we provide (tight) bounds on the speed of convergence; these bounds are polynomial in the number of players provided that the pressure grows sufficiently fast. We finally look beyond cliques: we characterize the graphs for which consensus is guaranteed, and make some considerations on the computational complexity of checking whether a graph satisfies such a condition. |
| Researcher Affiliation | Academia | Diodato Ferraioli University of Salerno, Italy dferraioli@unisa.it Carmine Ventre University of Essex, UK c.ventre@essex.ac.uk |
| Pseudocode | Yes | Algorithm 1: The dynamics |
| Open Source Code | No | The paper is theoretical and focuses on proofs and models. There is no mention or provision of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not involve experimental training with datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve validation datasets or splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical models and proofs, thus no software dependencies with version numbers are mentioned for replication. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training configurations. |